Myeloablative conditioning for allo-HSCT in pediatric ALL: FTBI or chemotherapy?—A multicenter EBMT-PDWP study
Bibliographic record
Abstract
Although most children with acute lymphoblastic leukemia (ALL) receive fractionated total body irradiation (FTBI) as myeloablative conditioning (MAC) for allogeneic hematopoietic stem cell transplantation (allo-HSCT), it is an important matter of debate if chemotherapy can effectively replace FTBI. To compare outcomes after FTBI versus chemotherapy-based conditioning (CC), we performed a retrospective EBMT registry study. Children aged 2-18 years after MAC for first allo-HSCT of bone marrow (BM) or peripheral blood stem cells (PBSC) from matched-related (MRD) or unrelated donors (UD) in first (CR1) or second remission (CR2) between 2000 and 2012 were included. Propensity score weighting was used to control pretreatment imbalances of the observed variables. 3.054 patients were analyzed. CR1 (1.498): median follow-up (FU) after FTBI (1.285) and CC (213) was 6.8 and 6.1 years. Survivals were not significantly different. CR2 (1.556): median FU after FTBI (1.345) and CC (211) was 6.2 years. Outcomes after FTBI were superior as compared with CC with regard to overall survival (OS), leukemia-free survival (LFS), relapse incidence (RI), and nonrelapse mortality (NRM). However, we must emphasize the preliminary character of the results of this retrospective "real-world-practice" study. These findings will be prospectively assessed in the ALL SCTped 2012 FORUM trial.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".